Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Mao, Qingqing; Jay, Melissa; Hoffman, Jana; Calvert, Jacob; Barton, Christopher; Shimabukuro, David; Shieh, Lisa; Chettipally, Uli K.; Fletcher, Grant; Kerem, Yaniv; Zhou, Yifan; Das, Ritankar

doi:10.1136/bmjopen-2017-017833

Cited by 249 publications

(264 citation statements)

References 26 publications

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“…2 The performance of the Sepsis-3 definition in clinical practice remains a point of discussion and contention, however, as there seems to be a loss of sensitivity relative to SIRS and variable performance based on practice setting (emergency department, inpatient ward, ICU). Furthermore, other novel models may provide even more accurate diagnosis and prediction 1415…”

Section: Definitionsmentioning

confidence: 99%

Sepsis associated acute kidney injury

Poston

Koyner

2019

BMJ

443

381

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Sepsis is defined as organ dysfunction resulting from the host’s deleterious response to infection. One of the most common organs affected is the kidneys, resulting in sepsis associated acute kidney injury (SA-AKI) that contributes to the morbidity and mortality of sepsis. A growing body of knowledge has illuminated the clinical risk factors, pathobiology, response to treatment, and elements of renal recovery that have advanced our ability to prevent, detect, and treat SA-AKI. Despite these advances, SA-AKI remains an important concern and clinical burden, and further study is needed to reduce the acute and chronic consequences. This review summarizes the relevant evidence, with a focus on the risk factors, early recognition and diagnosis, treatment, and long term consequences of SA-AKI. In addition to literature pertaining to SA-AKI specifically, pertinent sepsis and acute kidney injury literature relevant to SA-AKI was included.

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Section: Definitionsmentioning

confidence: 99%

Sepsis associated acute kidney injury

Poston

Koyner

2019

BMJ

443

381

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“…ML applied to heart rate and BP dynamics can independently predict sepsis 4 h prior to clinical onset . Similarly, InSight is an accurate ML based sepsis prediction algorithm that is robust to missing data and uses the change in vital signs over time to also accurately predict sepsis and severe sepsis hours prior to onset . A DL algorithm applied to electrocardiogram (ECG) analysis is able to detect 15 different arrhythmias, sinus rhythm and noise, more accurately than individual cardiologists…”

Section: Clinical Monitoringmentioning

confidence: 99%

“…24 Similarly, InSight is an accurate ML based sepsis prediction algorithm that is robust to missing data and uses the change in vital signs over time to also accurately predict sepsis and severe sepsis hours prior to onset. 25 A DL algorithm applied to electrocardiogram (ECG) analysis is able to detect 15 different arrhythmias, sinus rhythm and noise, more accurately than individual cardiologists. 26 Any system that attempts to give alerts must deal with the issue of false alarms that currently plague 'intelligent' monitors.…”

Section: Clinical Monitoringmentioning

confidence: 99%

Artificial intelligence and machine learning in emergency medicine

Stewart

Sprivulis

Dwivedi

2018

Emerg Medicine Australasia

104

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Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

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“…We limited tree branching to six levels, included no more than 1000 trees in the final ensemble, and set the XGBoost learning rate to 0.1. These hyperparameters were chosen to align with previous work and justified in the context of the present data with a coarse grid search [10].…”

Section: The Machine Learning Algorithmmentioning

confidence: 99%

“…The latest state of the MLA was characterized in the retrospective analysis. Previous states of the algorithm have been studied retrospectively and prospectively [10][11][12][13][14][15][16]; however, this study was performed on significantly larger and more diverse datasets.…”

Section: Introductionmentioning

confidence: 99%

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

Burdick

Pino

Gabel-Comeau

et al. 2018

Preprint

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KEY POINTSQuestion: Is a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?Findings: In a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset . In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate. Meaning:The accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes. ABSTRACTObjective: To validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.Setting: This cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.Participants: For retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included. Results:At severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P <.001), SOFA (0.74; P <.001), and SIRS (0.62; P <.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate. Conclusions:The MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.

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Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Cited by 249 publications

References 26 publications

Sepsis associated acute kidney injury

Sepsis associated acute kidney injury

Artificial intelligence and machine learning in emergency medicine

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

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